Predicting Seedling Emergence of Xanthium strumarium in Two Burial Depths

Increasing public awareness and concern about the impacts of herbicides on the environment, development of herbicide-resistant weeds, and high economic cost of herbicides have increased the need to reduce the amount of herbicides used in agriculture. Prediction of weed emergence timing would help reduce herbicide use through the optimization of the timing of weed control. There are several models that could be used for predicting weed seedling emergence. However, the ability to predict emergence of given species is different between models. For better prediction of emergence we should be able to select a suitable model. Therefore, Xanthium strumarium seedling emergence at two different burial depths from an experiment conducted in 2009-2010, was used to find and develop the best emergence model. The number of X. strumarium seedlings was recorded every three days and then removed from pots. Emergence for each species was expressed as a cumulative percentage of total emergences. Percentage of cumulative emergence values was explained against thermal time (TT) using Logistic, Gompertz and Weibull modified functions. The three models were compared using the Akaike information criterion. The Weibull model gave a better description than other models. Conversely, Logistic model gave the worst fit, with AIC values far higher than Weibull and Gompertz models. Thermal time required for given seedling emergence was affected by burial depth and increased with soil depth. For example, when seeds buried in the 5 cm depth, they required 744 TT for 50% emergence. However, seeds in 2 cm depth had a shorter emergence time-span and required 391-488 TT for 50% emergence